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In this work, we evaluate the use of a VGG11 convolutional neural network for bacterial classification from CPS images generated through numerical simulations, and we investigate training strategies aimed at enhancing robustness to realistic noise sources, including photon shot noise, dark current, and readout noise. We systematically analyze the effect of spatial down-sampling, L2 regularization, and alternative pixel-scaling strategies (linear vs. logarithmic) on classification accuracy. Our results show that dimensionality reduction significantly improves robustness to noise, whereas L2 regularization has an inconsistent impact on generalization. Logarithmic scaling stabilizes training but reduces noise tolerance, while linear scaling provides the opposite behaviour. Finally, we demonstrate that noise in the high-intensity regions contributes minimally to model performance, whereas it dominates in the low-intensity (idle) regions the VGG11\u2019s decision process. These results highlight the discriminative power of CPS and identify practical strategies to increase the robustness of deep-learning models trained on synthetic optical datasets.<\/jats:p>","DOI":"10.1007\/s00521-026-12054-7","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T04:58:32Z","timestamp":1776833912000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optical bacteria recognition: VGG-11 neural algorithms for pattern recognition"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1992-4345","authenticated-orcid":false,"given":"Riccardo","family":"Pepino","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Calabr\u00f2","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Fella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7724-4538","authenticated-orcid":false,"given":"Hamed","family":"Tari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4112-2544","authenticated-orcid":false,"given":"Alessandro","family":"Bile","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6106-461X","authenticated-orcid":false,"given":"Arif","family":"Nabizada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0995-0702","authenticated-orcid":false,"given":"Eugenio","family":"Fazio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"12054_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/pathogens10101310","volume":"10","author":"G Mancuso","year":"2021","unstructured":"Mancuso G, Midiri A, Gerace E, Biondo C (2021) Bacterial antibiotic resistance: the most critical pathogens. 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